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dc.contributor.author高允貞-
dc.creator高允貞-
dc.date.accessioned2016-08-25T04:08:27Z-
dc.date.available2016-08-25T04:08:27Z-
dc.date.issued1997-
dc.identifier.otherOAK-000000026001-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/181020-
dc.identifier.urihttp://dcollection.ewha.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000026001-
dc.description.abstractThis paper discusses determinants of layoffs and quits and how rent affects layoffs and quits There are not many researches on layoffs and quits in Korea. because layoffs rarely happens in Korea, if it would be happened it would not mean firm-initiated separations. So I divided esparations into two groups according to their wage growth; that is wage growth between jobs is higher for quits than for layoffs. The model is consistent with mant empirical regularities of the quit-layoff disticttion. Test results based on structural turn over model are as follows. In the study of empirical analysis, the result came out that education experience, union, race, marital status, children and unemployment rate affect layoffs and quits as I expected before; however industry and occupation do not affect layoffs and quits. In the <table 5-3>, government has positive effects on layoffs and quits, but in the <table 5-6>, it has negative effects on layoffs after I added outside wage offer to the variables in order to correct results. The firm size has negative effects on layoffs and quits. In the real statistics , it is also came out that the separation rate is 35% in the small firms (less than 199 people) but the separation rate is 19.6% in the large ones (more than 200 people). The main focus of my paper is how rent affects layoffs and quits. The parameter estimates in the <table 5-6> indicate that the separation rate is decreasing in the difference between opportunities within and outside the incumbent firm. The empirical problem in testing this implication of the theory arises because the data (KHPS-Korean household Panel Study) used in this research consist of data for only 4 years. In fact, four years is not enough time to see this data as the panel data. And another problem is that I used the proxy instead of experience because of data problem. I used age-education-8 instead of experience because there was no information about experience in the KHPS. And the way I divided separations into layoffs and quits according to wage growth could be a problem, because there might be a number of other reasons that cause layoffs and quits besides wage growth. Job search period can be another factor to divide separation, but I didin't deal with it inthis paper.-
dc.description.tableofcontents목차 Ⅰ. 서론 = 1 Ⅱ. 기존 연구에 관한 고찰 = 5 1. 노동 이론(labor turnover)에 대한 제이론 = 5 2. 이직과 해고에 관한 기존 논문 = 15 2.1 Parsons의 모형 = 17 2.2 Salop의 모형 = 19 2.3 Hashimoto & Yu의 모형 = 21 2.4 McLaughlin의 모형 = 26 Ⅲ. 모형 = 32 1. 모형 = 32 Ⅳ. 실증 분석 모형과 추정 방법 = 35 1. 실증 분석 모형 = 35 2. 추정 방법 = 35 Ⅴ. 추정 결과 = 43 1. 자료 = 43 2. 실증 분석 = 50 2.1 PROBIT 추정 결과 = 50 2.2 임금 함수 추정 = 55 Ⅵ. 요약과 결론 = 61 참고문헌 = 64 ABSTRACT = 68-
dc.formatapplication/pdf-
dc.format.extent1504344 bytes-
dc.languagekor-
dc.publisher이화여자대학교 대학원-
dc.subject이직-
dc.subject해고-
dc.subject경제학-
dc.subjectpanel-
dc.title우리나라의 이직과 해고에 대한 연구-
dc.typeMaster's Thesis-
dc.title.subtitlePanel 자료를 이용해서-
dc.title.translated(The) Analysis of layoffs and quits in Korea : used by the panel data-
dc.format.page70p.-
dc.identifier.thesisdegreeMaster-
dc.identifier.major대학원 경제학과-
dc.date.awarded1997. 8-
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